Ron Robins, MBA, Blog Author

For over forty years I have engaged in, and devoted myself to, the fields of economics, finance, and the development of human consciousness.

I'm deeply concerned about America's economic and financial problems and am writing a book on how I believe they can be fixed. The book's working title: "Resolving America's Economic Quagmire," with a subtitle, "People gaining inner fulfillment is the key.”

Statistics

Archive for the ‘Economic Measurement’ Category

Why do most of the methodologically revised U.S. economic statistics tend to create a picture of a more positive looking economy? Do these revised statistics really give a better—or illusory—understanding of economic activity? And is it coincidental that the benefits flowing from these more positive looking statistics largely accrue to powerful elites who also have the muscle to influence the statistical methodologies? Now those who should be investigating and informing us of these concerns, our economists and media, fail to do so.

We see this ‘positive bias’ appearing in the most important economic statistics, including unemployment rates, payroll numbers, the consumer price index (CPI), savings rates, and gross domestic product (GDP).

Considering the unemployment rates and payroll numbers, we find that the Bureau of Labor Statistics (BLS) has implemented many changes that have resulted in lower unemployment rates and higher payroll numbers.

One particular change in 1994 to the unemployment rate was most significant. At that time the BLS decided to exclude the long-term (over one-year) unemployed discouraged workers from measurement. The chart below, from ShadowStats, shows that revised rate, now referred to as the Official U3 rate, as the red line.

The unemployment rate including these long-term unemployed discouraged persons is the ShadowStats blue line. The broadest government unemployment rate U6 is the gray line, which ShadowStats says, includes “short-term discouraged and other marginally attached workers as well as those forced to work part-time because they cannot find full-time employment.”

Using March 2014 unemployment data, notice the huge difference in unemployment rates between the pre 1994 methodology, which ShadowStats estimates at 23.2%, and the much-publicized Official U3 rate of just 6.7% and U6 at 12.7%!

With reference to the BLS payrolls data, John Williams, ShadowStats founder, has regularly spotted “spurious revisions used to spike payroll employment levels.” He said of the March 2014 payroll report, that, “[The] increase of 192,000 was bloated heavily by concealed and constantly shifting seasonal adjustments… [that the] numbers remain of horrendous quality… generally not comparable with earlier reporting.”

Methodological changes to the CPI are also worrisome. Some non-government consumer price indices show exactly how much the government CPI has understated inflation that’s relevant to most people’s everyday experience. One such index is Guild Investment Management’s (GIM), Guild Basic Needs Index (GBNI). GIM says that because the BLS, “periodically alters its [CPI] content, making adjustments to the weighting of the components, and smoothing seasonal patterns. [That,] such tinkering with data… usually results in an understatement of the inflation rate and creates an unreliable, misleading cost of living index.”

The GBNI includes food, clothing, shelter and energy, covering 50-80% of most people’s expenditures. From the chart below see how over the five years to January 31, 2014, the annual increase in the GBNI was 4.7%, versus 2.1% for the CPI.

ShadowStats has re-worked the CPI as the BLS measured it with a fixed basket of goods in 1990 (see below), and in 1980 (not shown). Using the 1990 measure annual inflation in February 2014 was running at about 5%, blue line, versus under 2%, red line, for the Official CPI-U.

Changes to the personal savings rate methodology are of concern too. Negative personal savings rates in the past decade became positive. For instance, the personal savings rate (as a percentage of disposable personal income) in 2006 and 2007 was about -2% but has become +3% after revisions. Methodological changes in personal incomes and certain pension benefits, etc., had the effect of enhancing personal savings rates.

Regarding GDP, we see it has benefited from arguably bureaucratically lowered inflation rates. To arrive at ‘real’ U.S. GDP, the Bureau of Economic Analysis (BEA) reduces nominal (current prices) GDP by BEA’s own inflation measure. According to Mr. Williams, this measure shares many similarities to the CPI. One example is that it includes “quality-adjusted price indexes to deflate goods and services.” Hence, if a new computer has the same price as one several years ago but is many times more powerful, its price would now be deemed much, much lower, thereby lowering BEA’s price index and thus increasing real GDP.

To see exactly how these methodologies upwardly bias GDP, consider that BEA reported real GDP for 2013 at 1.9%. However, using the SGS-Alternate GDP that eliminates, as ShadowStats says, some of the “distortions in government inflation usage and methodological changes that have resulted in a built-in upside bias to official reporting,” real 2013 GDP would be about 4% lower and negative at around -2%!

These questionable brighter-looking statistics could be creating the illusion of a better economy. Coincidentally, such a possibly falsified, better-looking economy, greatly benefits some key political and financial elites who just happen to have disproportionate power to influence government statistical methods.

ShadowStats gives examples of the Johnson, Nixon, Carter, Reagan, Bush (first) and Clinton administrations engaging in acts to alter various economic statistics so as to put their respective administrations in a brighter light.

And the economic elite benefiting most from these more positive looking statistics pumping up the bond and stock markets are the ultra rich. Moreover, it is they who have an out sized influence on legislators and government policies and perhaps the most interest in adding gloss to the statistics.

Regrettably, those who should be critiquing and providing insight for the public about the meaning and consequences of the methodological changes to the statistics, our beloved economists, are missing-in-action. Economists, believing they are quasi-physicists of the economics realm, should be ashamed at their apparent near total public acquiescence to government statistical methods and methodological changes.

Sadly, the financial media is just as irresponsible too, parroting the statistical information spoon-fed to them by government. This is a situation suited to a dictatorship rather than an enlightened democracy.

When methodological changes to government economic statistics nearly always create a picture of a more positive economic reality, we have to doubt their integrity—especially when particularly powerful political and financial elites benefit the most from them. Alas, economists and financial journalists studiously avoid publicly critiquing the changing statistical methodologies. They treat government statistics as if they come down from God and written in stone. We deserve better in this enlightened age.

So, are these dubious, positively biased economic statistics providing improved insight into economic reality–or are they created to proffer the impression of a healthy economy?

By Ron Robins. First published June 6, 2011, in his weekly economics and finance column at alrroya.com

It is a simple statistic that continues to warn of huge economic problems ahead for the US. Some economists call it the ‘marginal productivity of debt (MPD).’ It relates the change in the level of all debt (consumer, corporate, government etc.) in a country to the change in its gross domestic product (GDP). However, due to the message it is delivering, most US economists employed in financial institutions, governments and private industry, as well as financiers and politicians, want to ignore it.

And for the US economy and government finances, the MPD (and related variants of it) is continuing to indicate extremely difficult economic times ahead.

I have vague recollections of the MPD concept from my economics classes long ago. But I was re-introduced to it around 2001 by a renowned economist who, during the following few years prior to his passing, became alarmed as to the MPD path of the US. His name was Dr. Kurt Richebächer, formerly chief economist and managing director of Germany’s Dresdner Bank. Dr. Richebächer, was so respected that former US Federal Reserve Chairman, Paul Volcker once said of him that, “sometimes I think that the job of central bankers is to prove Kurt Richebächer wrong,” reported the online financial journal, The Daily Reckoning on May 15, 2004.

Investigating Dr. Richebächer’s concern further, I wrote an article on my Enlightened Economics blog on January 23, 2008, titled, Is the Amazing US Debt Productivity Decline Coming to a Bad End? I found that, “for decades, each dollar of new debt has created increasingly less and less national income and economic activity. With this ‘debt productivity decline,’ new evidence suggests we could be near the end-game… ”

Another way of viewing the debt productivity problem is to look at it in terms of how many dollars of debt it took to help create total national income, which is the wages, salaries, profits, rents and interest income of everyone. Again, from my above mentioned article, which quotes Michael Hodges in his Total America Debt Report, that, “in 1957 there was $1.86 in debt for each dollar of net national income, but [by] 2006 there was $4.60 of debt for each dollar of national income – up 147 per cent. It also means this extra $2.74 of debt per dollar of national income produced zilch extra national income. In 2006 alone it took $6.32 of new debt to produce one dollar of national income.”

However, whereas the US private sector debt has marginally ‘de-leveraged’ (retrenched) since that crash (which might now be reversing), the US government, as everyone knows, has run up mammoth deficits to purportedly keep the country’s economy from imploding. Thus, the US’s MPD is marching to another, perhaps even more frightening tune, suggesting government financial insolvency and/or debt default.

One fascinating way of looking at the declining MPD of US government debt has just been presented by Rob Arnott on May 9, 2011, in his post, Does Unreal GDP Drive Our Policy Choices? What Mr. Arnott does is to subtract out the change in debt growth from GDP, and refers to this statistic as ‘Structural GDP.’ He finds that, “the real per capita Structural GDP, after subtracting the growth in public debt, remains 10 per cent below the 2007 peak, and is down 5 per cent in the past decade. Net of deficit spending, our prosperity is nearly unchanged from 1998, 13 years ago.”

In its effort to counter the significant economic difficulties since 2008, the US government has added, or will have added, around $4 trillion in deficits (financed by new debt) in its three fiscal years 2009, 2010 and 2011. Yet, all this massive government deficit spending has failed to really ignite economic growth. Most likely this is because of the enormous dead weight of unproductive and onerous private sector debt, particularly that of consumer debt. Hence, real US GDP will have increased probably less than $1.5trn during these years. Including some further economic benefit in the years thereafter, a total GDP benefit of only about $2trn is probable.

So, $4trn borrowed for $2trn in GDP gains. Thus, in very rough round numbers, each new one dollar of US government debt might only produce $0.50 in new economic activity and probably only about $0.08 in new federal tax revenue. (Federal tax revenue as a percentage of GDP is around 15 per cent.) Therefore, the economic marginal return for each new dollar of US government debt is possibly around -50 per cent! If you loaned someone $10 million and they gave you back $5m, you would not be happy!

Hence, it might not be long before those holding or buying US government bonds perceive the reality that the US government, and US economy, are losing massively on government borrowings. This will result in much, much higher US government bond yields and interest costs. Most importantly, it may make the rollover of US debt and new debt issuance incredibly difficult unless either US taxes rise stratospherically to cover the deficits, and/or the US Federal Reserve money printing goes into hyper-drive to purchase the debt the markets will not buy. (Of course US banks, pension funds etc., could also be forced to buy them.)

Thus, the idea that US government debt continues to be ‘risk-free’ is absurd.

For this, and for many other reasons cited above, is why the US financial and political elites want to keep hush-hush about what the MPD and its variants reveal!

By Ron Robins. First published April 21, 2011, in his weekly economics and finance column at alrroya.com

“…both risk models and econometric models… are still too simple to capture the full array of governing variables that drive global economic reality,” wrote Alan Greenspan, former chairman of the US Federal Reserve in the Financial Times on March 16, 2008. And if anyone should know about the quality and predictive validity of such models, it would be Mr. Greenspan. Time and again it has been shown that reliance on the predictions from such models is foolhardy.

It was the reliance on, and failure of their predictions, that caused enormous global financial and economic carnage in 2008 and 2009. Yet today dependence on these models seems greater than ever. I suggest our overt focus and use of them is often a wasted effort.

A truth that many modellers and their followers seem to have difficulty accepting is that the past—which most modellers use to prognosticate the future—has frequently been shown to be a poor basis upon which to determine future outcomes. Modellers can continue to refine their models in great detail, and then some unusual event occurs with a one in a million chance of happening—such as the US sub-prime mortgage fiasco—and their models fail. Sadly, the variables which may encompass a one in a million event are numerous. Among them are sudden changes of investor attitudes, weather patterns, geological events, and political and social upheavals.

If we look around today from the sudden movements in sovereign bond markets to the extraordinary weather recently in Australia, to the horrific Japanese earthquake, tsunami and nuclear reactor troubles, to the political upheavals in North Africa and the Middle East—all are kinds of exogenous events that can trash the predictions of the most exacting risk or econometric model.

Furthermore, a ‘perfect’ econometric model would only be possible, metaphorically speaking, if the modeller had ‘the mind of the creator.’ Only then perhaps, could all be known and predicted. Sadly—and I do not mean any disrespect to the modellers—I do not believe that many (if any) of them have that level of intelligence and consciousness at this time. So those constituencies that trust in these models are doomed to suffer continuing disappointments.

Another problem with these models is how to model for human behaviour, as it is both rational and irrational at different and unpredictable times. Therefore, before such modelling can ever hope to fully succeed, it must completely understand human consciousness: who we are, and how and why we act. And the modellers are a long, long way from such an understanding. Incidentally, there is a branch of economics, ‘behavioural economics,’ that is moving in that direction. I wish them good luck with that!

Economists today, unlike those of earlier eras, seem to believe that the only way they can be perceived as legitimate is to be scientifically oriented. Hence their passion for increasingly complex models and their statistician-like orientation.

The type of economic modelling that incorporates mathematics and statistical relationships to economic data, is termed econometrics. Google econometrics and you will probably find over 5,000,000 links. They are largely links to innumerable academics, research institutions, studies, papers and journals. With so much effort put into this field, any independent observer could conclude that econometrics must be a highly successful and seemingly scientific endeavour. It reminds me of the enormous quest for artificial intelligence (AI) to recreate the abilities of the human mind in computers. At least AI is somewhat plausible as it advances the field of computing and robotics which have many, many practical applications that we all know about.

But unlike AI research, economic and econometric models—with their significant variances and failures—have much less to offer society at this time. Mark Thoma, Professor of Economics at the University of Oregon offers these pertinent remarks in his blog, Economist’s View, on February 8. “Much of the uncertainty in economics derives from our inability to do laboratory experiments, and that includes uncertainty about which model best describes the macroeconomy. When the present crisis is finally over, those who advocated fiscal policy, those who advocated monetary policy, and those who advocated no policy at all will all say ‘I told you so’ based upon their reading of the evidence… the answers you get are only as good as the model used to get them, and considerable uncertainty remains over which macroeconomic model is best.”

In the 19th century’s Europe and North America, there were no econometric models (not in the way we know of them today), yet those continents experienced unprecedented economic growth. And the concept of gross domestic product (GDP)—which is usually a top concern in econometric modelling—was not created and used until World War II.

We know that econometric models are unreliable in providing information on how economies behave as well as their projections of future economic activity. Similarly, modelling for financial risk has been shown to be more than problematic and history shows reliance on risk models brings eventual failure and grief.

Therefore, given the facts, we need to be much, much less anxious about trying to create perfect risk and econometric models—and not rely on these models, generally. After all, it was mostly intuition and drive, not decisions based on risk and econometric models that led our greatest inventors, financiers, entrepreneurs and leaders to great success, thereby creating our modern economies.